scDTI
Single-cell Drug-Target Interaction
Task Overview
PyTDC introduces tdc_ml.scDTI task. The goal is to train a model for predicting the probability that a protein is a candidate therapeutic target in a specific cell type. The model learns an estimator for a function of a protein target and a cell-type-specific biological context as input, and the model is tasked to predict the probability the candidate protein is a therapeutic target in that cell type.
Impact
Single-cell data have enabled the study of gene expression and function at the level of individual cells across healthy and disease states. To facilitate biological discoveries using single-cell data, machine-learning models have been developed to capture the complex, cell-type-specific behavior of genes. In addition to providing the single-cell measurements and foundation models, PyTDC supports the development of contextual AI models to nominate therapeutic targets in a cell type-specific manner.
Generalization
Models are expected to have strong performance on cell-context-specific evaluation metrics across different sets of disease-specific proteins and cells.
Product
Small-molecule.
Pipeline Stage
Nomination / Identification.
Available Datasets
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.multi_pred import scDTI
# Load a dataset
data = scDTI(name='opentargets_dti')
# Access the data
df = data.get_data()
print(df.head())
# Get train/val/test splits
split = data.get_split()
print(split)